sigPCA: Statistical Significance Testing for Principal Components

Identifies principal components whose eigenvalues exceed those expected under noise. Implements analytical thresholds derived from the Marchenko-Pastur distribution (Marchenko and Pastur, 1967) <doi:10.1070/SM1967v001n04ABEH001994> and empirical permutation tests, and provides functions for visualizing observed and null eigenvalue spectra.

Version:	0.1.0
Imports:	ggplot2, stats
Suggests:	knitr, palmerpenguins, rmarkdown, testthat (≥ 3.0.0)
Published:	2026-08-03
DOI:	10.32614/CRAN.package.sigPCA (may not be active yet)
Author:	Guillermo de Anda-Jáuregui [aut, cre, cph], Enrique Hernández-Lemus [aut, cph]
Maintainer:	Guillermo de Anda-Jáuregui <gdeanda at inmegen.edu.mx>
BugReports:	https://github.com/guillermodeandajauregui/sigPCA/issues
License:	MIT + file LICENSE
URL:	https://github.com/guillermodeandajauregui/sigPCA
NeedsCompilation:	no
Materials:	README
CRAN checks:	sigPCA results

Documentation:

Reference manual:	sigPCA.html , sigPCA.pdf
Vignettes:	Finding structure in iris with sigPCA (source, R code) Finding structure in Palmer penguins with sigPCA (source, R code) sigPCA: Statistical Significance Testing for Principal Components (source, R code)

Downloads:

Package source:	sigPCA_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: sigPCA_0.1.0.zip
macOS binaries:	r-release (arm64): sigPCA_0.1.0.tgz, r-oldrel (arm64): not available, r-release (x86_64): sigPCA_0.1.0.tgz, r-oldrel (x86_64): sigPCA_0.1.0.tgz

Linking:

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